A Virtual Reality Environment for Genomic Data Visualization
A Virtual Reality Environment for Genomic Data Visualization
批准号:
7218900
负责人:
Fatima Aziz Merchant
金额:
$12.37万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2007
资助国家:
美国
项目状态:
已结题
起止时间:
2007-09-13 至 2009-05-31
关键词:
3-DimensionalArchitectureAreaBehaviorBioinformaticsBiomedical ResearchChemicalsClassCognitionColorComplexComputer softwareDNADNA Microarray ChipDNA Microarray formatDataData AnalysesData DisplayDatabasesDevelopmentDiagnosticDimensionsDiscriminant AnalysisDiseaseEnvironmentFaceFocus GroupsGene ExpressionGenesGeneticGenomicsHumanImageryInvestmentsMapsMarketingMethodsMolecular BiologyNumbersOutcomePattern RecognitionPattern Recognition SystemsPerceptionPharmaceutical PreparationsPharmacologic SubstancePhasePhase I Clinical TrialsPliabilityPositioning AttributePrimary Health CarePrincipal Component AnalysisProteomicsRateResearchResearch PersonnelScienceScientistShapesStatistical MethodsSystemTechniquesTechnologyTextureTimeWalkingWorkabstractingbasecommercial applicationcostcost effectivedata miningdesigndrug discoveryimprovedindependent component analysisinnovationinstrumentmecarzolenext generationprototyperesearch facilityresearch studysizetechnological innovationtheoriestoolvirtualvirtual reality
中文摘要
描述(申请人提供):该项目将开发一个新的虚拟现实显示环境,用于可视化来自基因组实验的多通道、多维数据。基因组学和DNA微阵列的出现极大地改变了研究的局面。研究人员现在必须筛选大量的实验数据,以检测基因组之间的关系。因此,需要新的数据可视化工具来支持现代科学。我们的方法是将统计数据投影与虚拟现实(VR)显示技术灵活地结合起来,将N维数据(3=N)投影到M维(3=M=10),并在3-D虚拟空间中显示给调查人员。该系统的一个独特之处在于,它将为研究人员提供在多种数据投影技术中进行选择的选项,包括主成分分析(PCA)、线性判别分析(LDA)、奇异值分解(SVD)、多维尺度(MDS)、投影寻踪(PP)、自组织映射(SOM)、独立成分分析(ICA)等。数据将显示为漂浮在空间中的标志性对象,这些对象在大小、形状、颜色、纹理、位置、方向和旋转方面都不同。在这项第一阶段研究中,VR显示器将作为基于工作站的飞行环境来实施。第一阶段项目的目的是清楚地展示基于VR的数据可视化工具在基因组数据分析中的价值。如果第一阶段成功,将在第二阶段开发和评估VR漫游沉浸式环境。由此产生的商业硬件/软件包将提供显著改进的科学数据可视化和解释,以帮助基因组学、蛋白质组和生物信息学研究人员以及模式识别系统开发人员解释他们极其复杂的实验数据。由此产生的商业硬件/软件包将提供显著改进的科学数据可视化和解释,以帮助基因组学、蛋白质组学和生物信息学研究人员以及模式识别系统开发人员解释他们极其复杂的实验数据。这一发展将带来的基本医疗福利将来自疾病诊断和治疗的加速发展。
英文摘要
DESCRIPTION (provided by applicant): This project will develop a new virtual reality display environment for visualization of multi-channel, multi- dimensional data from genomic experiments. The advent of genomics and DNA micro arrays has changed the situation in research drastically. Researchers now must sift through reams of experimental data to detect relationships among groups of genes. Therefore, new data visualization tools are required to support modern science. Our approach is to combine statistical data projection with virtual reality (VR) display technology in a flexible way that projects N-dimensional data (3=N) to M dimensions (3=M=10), and displays it to the investigator in a 3-D virtual space. A unique feature of the proposed system is that it will provide the investigator the option to select among a number of data projection techniques, including principal component analysis (PCA), linear discriminant analysis (LDA), singular value decomposition (SVD), multi-dimensional scaling (MDS), projection pursuit (PP), self-organizing maps (SOMs), independent component analysis (ICA), and others. Data will be displayed as iconic objects floating in space that differ in size, shape, color, texture, position, orientation and spin. The VR display will be implemented as a workstation-based fly-through environment in this Phase I study. The aim of the Phase I project is to demonstrate clearly the value of VR- based data visualization tools in genomic data analysis. If Phase I is successful, a VR walk-through immersive environment will be developed and evaluated in Phase II. The resulting commercial hardware/software package will provide significantly improved scientific data visualization and interpretation to assist genomics, proteomics and bioinformatics researchers, and pattern recognition system developers in the interpretation of their overwhelmingly complex experimental data. The resulting commercial hardware/software package will provide significantly improved scientific data visualization and interpretation to assist genomics, proteomics and bioinformatics researchers, and pattern recognition system developers in the interpretation of their overwhelmingly complex experimental data. The primary healthcare benefits that will accrue from this development will result from the accelerated development of diagnostics and treatments for diseases.
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